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Ensemble Learning for Object Recognition and Tracking

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Pattern Recognition, Machine Intelligence and Biometrics

Abstract

Nowadays, a simple query in Google image search will return millions of results. Apparently this is not because existing vision technologies for object recognition are performing at such a high-level4; on the contrary, while human beings can recognize objects with little difficulty, artificial vision systems are far from matching the accuracy, speed and generality of human vision [1]. Potential applications enabled by solving the object recognition problem are enormous, content-based image retrieval, object tracking, robot navigation, automated surveillance, etc. are some that come to mind. Object recognition is usually described as a high-dimensional classification problem where only a small number of training samples (compared to the data dimensionality) are available. Consequently, it is extremely difficult, if not impossible, to construct an efficient single classification rule. Ensemble learning is a method for constructing accurate classifiers from an ensemble of weak predictors or base classifiers. During the past decade, ensemble learning methods have been extensive studied for various disciplines. In this chapter, we review influential works along recent topics of ensemble learning approaches devised for recognizing and tracking objects.

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Author information

Authors and Affiliations

  1. NICTA, PO Box 6020, St Lucia, QLD, 4067, Australia

    Mehrtash Harandi & Brian C. Lovell

  2. School of ITEE, The University of Queensland, QLD, 4072, Australia

    Mehrtash Harandi & Brian C. Lovell

  3. School of Information Technologies, The University of Sydney, NSW, 2006, Australia

    Javid Taheri

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  1. Mehrtash Harandi
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  2. Javid Taheri
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  3. Brian C. Lovell
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Editors and Affiliations

  1. CCIS, Northeastern University, 360 Huntington Ave, Boston, MA, 02110, USA

    Patrick S. P. Wang

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© 2011 Higher Education Press, Beijing and Springer-Verlag Berlin Heidelberg

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Harandi, M., Taheri, J., Lovell, B.C. (2011). Ensemble Learning for Object Recognition and Tracking. In: Wang, P.S.P. (eds) Pattern Recognition, Machine Intelligence and Biometrics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22407-2_11

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  • DOI: https://doi.org/10.1007/978-3-642-22407-2_11

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